# Picasso: Model-free Feature Visualization

arXiv: https://arxiv.org/abs/2111.12795 · HTML: https://mlsanity.com/paper/2111.12795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Understanding Neural Networks via Feature Visualization: A survey](https://mlsanity.com/paper/1904.08939/) (2019)
- [Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform](https://mlsanity.com/paper/1908.05376/) (2019)

## Built on by (most cited first, 1 of 1)

- [Efficient Multi-stage Inference on Tabular Data](https://mlsanity.com/paper/2303.11580/) (2023)

## Abstract (arXiv)

Today, Machine Learning (ML) applications can have access to tens of thousands of features. With such feature sets, efficiently browsing and curating subsets of most relevant features is a challenge. In this paper, we present a novel approach to visualize up to several thousands of features in a single image. The image not only shows information on individual features, but also expresses feature interactions via the relative positioning of features.